Naive Bayes is a classification technique that is based on Bayes’ Theoremwith an assumption that all the features that predicts the target value are independent of each other. It calculates the ... In this guide, you'll learn exactly how the Naive Bayes classifier works, why it's so effective despite its simplicity, and how you can apply it and more. What Is the Naive Bayes Classifier Algorithm? The Naive Bayes classifier algorithm is a machine learning technique used for classification tasks. It is based on Bayes’ theorem and assumes that features are conditionally independent of each other given the class label. The algorithm calculates the probability of a data point belonging to each class and assigns it to the class with the highest probability. Naive Bayes is known for its simplicity, efficiency, and effectiveness in handling ... What is Naive Bayes? Naive Bayes is a supervised machine learning algorithm that uses Bayes’ Theorem with a key assumption: all features are conditionally independent given the class label. Despite this “naive” assumption, the algorithm often performs remarkably well. It is a probabilistic classifier, meaning it estimates the probability that a data point belongs to a certain class, then selects the class with the highest probability.
